Its an abstraction for neural networks. Different individual networks might vary in number of layers (columns), nodes (circles), or loss function (lines), but the concept is consistent across all.
I haven’t really done Neural Networks in 2 decades, and was under the impression that NNs pretty much dominate Machine Learning nowadays, whilst stuff like Genetic Algorithms were way less popular or not at all used anymore.
Neural networks are a class of models. Genetic algorithms are a class of learning algorithms. You use learning algorithms to train models. Genetic algorithms are a valid way of training neural networks, but this is not currently in vogue. They’re typically trained via gradient descent.
I don’t know if it is the case for the world today, but all those Models behave like genetic algorithms and IF-functions with a little RNG sprinkled on top of them.
You mean that they’re actually competing multiple variants of a model against each other to see which ones get closer to generating the expected results, and picking the best ones to create the next generation?
Because that’s how Genetic Algorithms work and get trained, which is completelly different from how Neural Networks work and get trained.
Also the links in Neural Networks don’t at all use IF-functions: the output of a neuron is just a mathematical operation on the values of all its inputs (basically a sum of the results of a function applied to the input numbers, though nowadays there are also cyclic elements) - the whole thing is just floating values being passed down the network (or back up the network during training) whilst being transformed by some continuous function or other with no discontinuity like you would get with IF involved.
Not all machine learning is AI. There are plenty of Machine Learning algorithms like Random Forests that are not neural networks. Deep learning would be big neural networks.
LLM models or ML in general? i’ve only known folk who worked on ML, not actually done it myself
LLMs definitely, but not all machine learning uses neural networks
ohhhh so that’s The model for neural networks, not A model for neural networks
It’s only one type of neural network. A dense MLP. You have sparse neural networks, recurrent neural networks, convolutional neural networks and more!
Its an abstraction for neural networks. Different individual networks might vary in number of layers (columns), nodes (circles), or loss function (lines), but the concept is consistent across all.
Kinda but also no. That’s specifically a dense neural network or MLP. It gets a lot more complicated than that in some cases.
I haven’t really done Neural Networks in 2 decades, and was under the impression that NNs pretty much dominate Machine Learning nowadays, whilst stuff like Genetic Algorithms were way less popular or not at all used anymore.
Is that the case?
Neural networks are a class of models. Genetic algorithms are a class of learning algorithms. You use learning algorithms to train models. Genetic algorithms are a valid way of training neural networks, but this is not currently in vogue. They’re typically trained via gradient descent.
I don’t know if it is the case for the world today, but all those Models behave like genetic algorithms and IF-functions with a little RNG sprinkled on top of them.
You mean that they’re actually competing multiple variants of a model against each other to see which ones get closer to generating the expected results, and picking the best ones to create the next generation?
Because that’s how Genetic Algorithms work and get trained, which is completelly different from how Neural Networks work and get trained.
Also the links in Neural Networks don’t at all use IF-functions: the output of a neuron is just a mathematical operation on the values of all its inputs (basically a sum of the results of a function applied to the input numbers, though nowadays there are also cyclic elements) - the whole thing is just floating values being passed down the network (or back up the network during training) whilst being transformed by some continuous function or other with no discontinuity like you would get with IF involved.
Not all machine learning is AI. There are plenty of Machine Learning algorithms like Random Forests that are not neural networks. Deep learning would be big neural networks.
The other way around. Machine learning is a subset of AI.